Predicting PM10 from PM2.5 and NO2 Levels in Metropolitan Lima, Peru: A Regularized Regression Approach

Centeno J.C.; Cerron H.L.; Matias J.R.; Soria-Quijaite J.J.; Saboya N. · International Journal of Advanced Computer Science and Applications (2026) · DOI: 10.14569/ijacsa.2026.0170887

Resumen

Suspended particle pollution (PM10) represents a serious public health problem in Metropolitan Lima, since its high levels are related to an increase in respiratory and heart diseases. This work aims to anticipate PM10 concentrations in seven districts of Lima using a multiple linear regression (OLS) model with the Ridge and Lasso regularization techniques. PM2.5 and NO2 were selected as predictors because they were the only pollutant variables available in the SENAMHI monitoring dataset; hourly records collected between 2015 and 2024 were used, yielding 169,308 observations after removing missing values and outliers. The data were separated into 80% for training and 20% for validation, applying cross-validation to determine the most appropriate regularization parameter. The final model reached a coefficient of determination (R2) of 0.23 and a mean squared error (MSE) of 521 in the test set. The findings indicate that PM2.5 is the main factor that predicts PM10, while NO2 exerts a secondary impact. Although the predictive capacity of the models is limited, the use of both Ridge and Lasso helped stabilize the coefficients and minimize overfitting. Given this limited explanatory power, the model should be regarded as an initial, interpretable baseline for PM10 monitoring in Metropolitan Lima rather than an operational forecasting tool. It is suggested to include meteorological variables and investigate non-linear models in future studies to enhance the accuracy and usefulness of the model in air quality management. © (2026), (Science and Information Organization). All Rights Reserved.

Instituciones: Universidad Peruana Unión